G.C.H.E. de Croon
200 records found
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CUAHN-VIO
Content-and-uncertainty-aware homography network for visual-inertial odometry
Learning-based visual ego-motion estimation is promising yet not ready for navigating agile mobile robots in the real world. In this article, we propose CUAHN-VIO, a robust and efficient monocular visual-inertial odometry (VIO) designed for micro aerial vehicles (MAVs) equipped w
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MAVRL
Learn to Fly in Cluttered Environments With Varying Speed
Autonomous flight in unknown, cluttered environments is still a major challenge in robotics. Existing obstacle avoidance algorithms typically adopt a fixed flight velocity, overlooking the crucial balance between safety and agility. We propose a reinforcement learning algorithm t
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Aggressive time-optimal control of quadcopters poses a significant challenge in the field of robotics. The state-of-the-art approach leverages reinforcement learning (RL) to train optimal neural policies. However, a critical hurdle is the sim-to-real gap, often addressed by emplo
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Heading estimation is vital for the autonomous flight of unmanned aerial vehicles. Magnetometers are typically used for this purpose, but they are not robust to electro-magnetic interferences. As a promising alternative, we investigate the insect-inspired solution of skylight pol
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ROVIO is one of the state-of-the-art monocular visual inertial odometry algorithms. It uses an Iterative Extended Kalman Filter (IEKF) to align visual features and update the vehicle state simultaneously by including the feature locations in the state vector of the IEKF. This alg
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Unmanned air vehicles (UAVs) have traditionally been considered as "eyes in the sky", that can move in three dimensions and need to avoid any contact with their environment. On the contrary, contact should not be considered as a problem, but as an opportunity to expand the range
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Optical flow-based control for micro air vehicles
An efficient data-driven incremental nonlinear dynamic inversion approach
This paper proposes an innovative approach for optical flow-based control of micro air vehicles (MAVs), addressing challenges inherent in the nonlinearity of optical flow observables. The proposed incremental nonlinear dynamic inversion (INDI) control scheme employs an efficient
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Biological sensing and processing is asynchronous and sparse, leading to low-latency and energy-efficient perception and action. In robotics, neuromorphic hardware for event-based vision and spiking neural networks promises to exhibit similar characteristics. However, robotic imp
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Insects have long been recognized for their ability to navigate and return home using visual cues from their nest's environment. However, the precise mechanism underlying this remarkable homing skill remains a subject of ongoing investigation. Drawing inspiration from the learnin
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This Review discusses the main results obtained in training end-to-end neural architectures for guidance and control of interplanetary transfers, planetary landings, and close-proximity operations, highlighting the successful learning of optimality principles by the underlying ne
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In recent years, Artificial Neural Networks (ANN) have become a standard in robotic control. However, a significant drawback of large-scale ANNs is their increased power consumption. This becomes a critical concern when designing autonomous aerial vehicles, given the stringent co
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Navigation is an essential capability for autonomous robots. In particular, visual navigation has been a major research topic in robotics because cameras are lightweight, power-efficient sensors that provide rich information on the environment. However, the main challenge of visu
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Developing optimal controllers for aggressive high-speed quadcopter flight poses significant challenges in robotics. Recent trends in the field involve utilizing neural network controllers trained through supervised or reinforcement learning. However, the sim-to-real transfer int
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Inspired by frame-based methods, state-of-the-art event-based optical flow networks rely on the explicit construction of correlation volumes, which are expensive to compute and store, rendering them unsuitable for robotic applications with limited compute and energy budget. Moreo
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Accurate trajectory tracking with quadrotors is a challenging task that requires a trade-off between accuracy and complexity to run onboard. Stateof- the-art adaptive controllers achieve impressive trajectory tracking results with slight performance degradation in varying winds o
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NanoFlowNet
Real-time Dense Optical Flow on a Nano Quadcopter
Nano quadcopters are small, agile, and cheap platforms that are well suited for deployment in narrow, cluttered environments. Due to their limited payload, these vehicles are highly constrained in processing power, rendering conventional vision-based methods for safe and autonomo
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AvoidBench
A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors
Obstacle avoidance is an essential topic in the field of autonomous drone research. When choosing an avoidance algorithm, many different options are available, each with their advantages and disadvantages. As there is currently no consensus on testing methods, it is quite challen
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The authors regret to inform that an incorrect transfer function was included in Eq. (12). The correct transfer function is: [Formula Presented] The selected gains were [Formula Presented]. This leads to a real pole at 0.964 and two complex poles at 0.965 ± 0.0445i. The differenc
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Neuromorphic processing promises high energy efficiency and rapid response rates, making it an ideal candidate for achieving autonomous flight of resource-constrained robots. It can be especially beneficial for complex neural networks as are used for high-level visual perception.
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